
quick guide to solving seasonal adjustment problems 261
Diagnostic: What it does: How to fix it in the event of a failure:
M1Shows how large the irregular com-
ponent is compared to the seasonal
component
May need to consider suitability
of prior adjustments (outliers, level
shifts etc) or the need for such adjust-
ments to be added
M2Measures the contribution of the ir-
regular component to the variance of
the raw series (transformed to a sta-
tionary series) May need to consider
suitability of prior adjustments (out-
liers, level shifts etc) or the need for
such adjustments to be added
M3Measures the amount of period-to-
period change in the irregular com-
pared to that in the trend.
May need to consider suitability
of prior adjustments (outliers, level
shifts etc) or the need for such adjust-
ments to be added
M4A measure of autocorrelation in the
irregular component.
Consider different moving averages
M5Measures the irregular compared to
the trend
May need to consider suitability
of prior adjustments (outliers, level
shifts etc) or the need for such adjust-
ments to be added
M6Also measures the irregular but is
only valid when a 3x5seasonal filter
is used.
A seasonal filter shorter than 3x5
should be used
M7Shows the amount of moving com-
pared to stable seasonality (basically
how regular the seasonal pattern is)
Suggests the series is not seasonal, or
that seasonality cannot be identified.
M8The size of the fluctuations in the
seasonal component throughout the
whole series
May indicate the presence of a sea-
sonal break or the need for a change
of moving average
M9The average linear movement in the
seasonal component throughout the
whole series
May indicate the presence of a sea-
sonal break or the need for a change
of moving average
M10 The size of the fluctuations in the
seasonal component for recent years
only
May need to consider a change to the
ARIMA model or consider the pres-
ence of a seasonal break within recent
years
M11 The average linear movement in the
seasonal component for recent years
only
May need to consider a change to the
ARIMA model or consider the pres-
ence of a seasonal break within recent
years
Table 26.1: M-statistic meanings and suggestions
1.250 for monthly series or 1.050 for quarterly series, then this suggests
that the series may not be seasonal. See Chapter 16 for more information.
Another way of checking if a series is seasonal is to use the sliding span
diagnostic. This may indicate if the series is becoming non-seasonal/seasonal.
See Chapter 18 for more information. It is also possible to compare the
standard deviations given in the E5and E6outputs. E5shows the month-
to-month percent change in the original series and E6shows the month-to-
month percent change in the seasonally adjusted series. Both of which give
the standard deviation of the respective series under the tables provided.
261